Compliance & Data Governance

Vantage provides purpose-built nodes for compliance — including AI Compliance Check, Data Validation, Data Classify, and Compliance Check — that let you build automated policy evaluation pipelines. Every workflow execution is logged and auditable, making Vantage a natural foundation for governance programs.

Evaluate Policies Automatically and Build Audit Trails

Run every record through configurable compliance policies and generate an auditable evaluation trail.

Scenario: A financial services firm must evaluate every customer transaction against AML (Anti-Money Laundering) and KYC (Know Your Customer) policies on a daily basis and produce evidence for regulators.

Workflow Steps:

1. Schedule Trigger — Run daily at 2 AM (off-peak)
2. Database Query (MSSQL) — Pull all transactions from the past 24 hours with customer ID, amount, origin country, and counterparty details
3. Database Query (PostgreSQL) — Cross-reference customer records for KYC status, risk tier, and last verification date
4. Join — Merge transactions with customer profiles on customer ID
5. AI Compliance Check — Evaluate each record against:
   • AML thresholds: transaction amounts, velocity, structuring patterns
   • KYC freshness: is the customer's identity verification current?
   • Sanctions screening: counterparty against OFAC/EU sanctions lists
   • Returns: PASS, FAIL, or REVIEW with detailed reasoning
6. Multi-Conditional — Route by result:
   • PASS → DB Write (mark as compliant in audit table) + Dashboard Output
   • FAIL → Send Email to compliance officer with flagged transaction details + Create Issue (Jira) for investigation + DB Write (quarantine)
   • REVIEW → Dashboard Output to Table Tile (manual review queue) + Send Message (Teams) to compliance analyst
7. Aggregation — Summarize: total evaluated, pass count, fail count, review count, fail rate %
8. Write PDF — Generate daily compliance evaluation report with policy names, outcomes, and exception details
9. DB Write — Log complete audit trail: evaluation timestamp, policy version, outcome, reviewer (automated/manual)
10. Dashboard Output — Populate:
    • Metric Tile — Pass rate % with trend
    • Bar Tile — Failures by policy category
    • Event Feed Tile — Real-time compliance violation log
    • Table Tile — Manual review queue with sortable columns
    • Waterfall Tile — Records flow: total → pass → fail → review

Key Nodes: Schedule Trigger, Database Query, Join, AI Compliance Check, Multi-Conditional, DB Write, Aggregation, Write PDF, Create Issue (Jira), Send Email, Send Message, Dashboard Output

Classify Data and Apply Sensitivity Labels

Automatically classify data records by sensitivity level and enforce handling policies.

Scenario: An organization needs to label all data assets with sensitivity classifications (Public, Internal, Confidential, Restricted) to comply with data protection regulations (GDPR, CCPA).

Workflow Steps:

1. Schedule Trigger — Run weekly
2. Database Query (PostgreSQL) — Pull metadata for all tables and columns in the data warehouse: table name, column name, sample values, data types
3. Data Classify — Apply classification rules:
   • PII detection: names, emails, SSNs, addresses, phone numbers
   • Financial data: account numbers, credit card numbers, transaction amounts
   • Health data: diagnoses, medications, lab results
   • Assigns: PUBLIC, INTERNAL, CONFIDENTIAL, or RESTRICTED
4. AI Enrichment — For ambiguous cases, use AI to determine classification based on column name patterns and sample value analysis
5. Data Validation — Verify that classifications are consistent (e.g., a column classified as RESTRICTED is not in a publicly accessible table)
6. Multi-Conditional — Route by classification:
   • RESTRICTED or CONFIDENTIAL → Send Email to data steward with access review request + DB Write (update data catalog)
   • INTERNAL → DB Write (update data catalog)
   • PUBLIC → DB Write (update data catalog)
7. Dashboard Output — Populate:
   • Pie Tile — Distribution of data by classification level
   • Table Tile — Complete data catalog with classification, owner, and last reviewed date
   • Pyramid Tile — Data sensitivity hierarchy
   • Metric Tile — % of data classified, % overdue for review

Key Nodes: Schedule Trigger, Database Query, Data Classify, AI Enrichment, Data Validation, Multi-Conditional, DB Write, Send Email, Dashboard Output

Generate and Submit Regulatory Reports

Automate the generation of compliance reports required by regulatory bodies.

Scenario: A public company must generate SOX (Sarbanes-Oxley) control test reports monthly, consolidating data from multiple systems and producing auditor-ready documentation.

Workflow Steps:

1. Schedule Trigger — Run on the 1st of every month
2. Database Query (MSSQL) — Pull financial transactions from the ERP
3. Database Query (PostgreSQL) — Pull access control logs: who accessed what systems, when, and what actions they took
4. Database Query (MongoDB) — Pull change management records: system changes, approvals, deployment dates
5. Join — Link financial transactions to the users who processed them and the system changes that occurred during the period
6. Compliance Check — Evaluate each control:
   • Separation of duties: users who approved and processed the same transaction?
   • Access appropriateness: users with access beyond their role?
   • Change management: were all system changes approved before deployment?
7. Aggregation — Summarize control test results by control category and risk level
8. AI Summary — Generate a narrative summary of findings: "Control 3.2 (Separation of Duties) passed for 98.7% of transactions. Two exceptions were identified involving user J. Smith, who both approved and posted journal entries on Jan 15 and Jan 22."
9. Write PDF — Generate a formatted SOX control test report with executive summary, detailed findings, and appendices
10. Write Excel — Generate a workbook with supporting evidence: transaction listings, access logs, exception details
11. Send Email (Outlook) — Distribute to the CFO, internal audit team, and external auditors
12. Dashboard Output — Populate:
    • Step Tile — Control test completion progress
    • Bar Tile — Pass/fail by control category
    • BI Intelligence Tile — AI-generated executive findings narrative
    • Comparison Tile — This month vs. previous month control results

Key Nodes: Schedule Trigger, Database Query (MSSQL, PostgreSQL, MongoDB), Join, Compliance Check, Aggregation, AI Summary, Write PDF, Write Excel, Send Email, Dashboard Output

Enforce Data Quality Governance Continuously

Monitor data quality metrics across all critical datasets and enforce quality SLAs.

Scenario: A data governance team has defined data quality SLAs (e.g., >99% completeness, >98% accuracy, <0.1% duplicate rate) and needs continuous monitoring with automated escalation.

Workflow Steps:

1. Schedule Trigger — Run every 6 hours
2. Database Query (PostgreSQL) — Run quality check queries against each critical dataset:
   • Completeness: SELECT COUNT(*) WHERE required_field IS NULL
   • Uniqueness: SELECT COUNT(*) - COUNT(DISTINCT key_field)
   • Freshness: SELECT MAX(updated_at) — is data current?
   • Validity: Range and format checks
3. Aggregation — Calculate quality scores by dataset: completeness %, uniqueness %, freshness (hours since last update), validity %
4. Computed Column — Calculate composite data quality index (DQI): weighted average across all dimensions
5. Filter — Datasets below SLA thresholds
6. Multi-Conditional — Route by severity:
   • DQI < 90% (Critical) → Send Email to data governance lead + Send Message (Slack #data-quality) + Create Issue (Jira) for remediation
   • DQI 90–95% (Warning) → Send Message (Slack) to data steward + Dashboard Output to watchlist
   • DQI ≥ 95% (Healthy) → Dashboard Output only
7. Dashboard Output — Populate:
   • Metric Tile — Overall enterprise DQI with sparkline
   • Table Tile — Dataset quality scorecard (one row per dataset)
   • Line Tile — DQI trends over the last 30 days
   • Event Monitor Tile — Real-time SLA breach alerts
   • Comparison Tile — This quarter vs. last quarter quality scores

Key Nodes: Schedule Trigger, Database Query, Aggregation, Computed Column, Filter, Multi-Conditional, Create Issue (Jira), Send Email, Send Message, Dashboard Output

Example Dashboard: Compliance & Governance Center

Build this dashboard to provide your compliance team and auditors real-time visibility into policy evaluation, data quality, and regulatory readiness.

Row 1 — Compliance Health

Row 2 — Policy Evaluation

Row 3 — Audit Trail & Queue

Row 4 — Data Classification & Quality

Row 5 — Reporting & Comparison

Getting Started

To build your compliance automation:

1. Define your policies — Document the rules you need to evaluate (AML thresholds, access policies, quality SLAs)
2. Connect your databases — Add your transactional, audit, and access-log databases under Integrations
3. Build evaluation workflows — Use AI Compliance Check or Compliance Check nodes with your policy definitions
4. Route results — Add Multi-Conditional to separate PASS/FAIL/REVIEW outcomes
5. Generate evidence — Use Write PDF and Write Excel to produce audit-ready documentation
6. Monitor continuously — Build dashboards with Metric, Bar, and Event Feed tiles to track compliance health in real time